Secure Speaker Recognition using BGN Cryptosystem with Prime Order Bilinear Group

2015 ◽  
Vol 9 (4) ◽  
pp. 1-19
Author(s):  
S. Selva Nidhyananthan ◽  
Prasad M. ◽  
Shantha Selva Kumari R.
Keyword(s):  
Speaker Recognition ◽  
Execution Time ◽  
Prime Order ◽  
Speaker Identification ◽  
Speaker Verification ◽  
Recognition System ◽  
Multiparty Computation ◽  
Speech Input ◽  
Order Group ◽  
Main Disadvantage

Speech being a unique characteristic of an individual is widely used in speaker verification and speaker identification tasks in applications such as authentication and surveillance respectively. In this paper, framework for secure speaker recognition system using BGN Cryptosystem, where the system is able to perform the necessary operations without being able to observe the speech input provided by the user during speaker recognition process. Secure speaker recognition makes use of Secure Multiparty Computation (SMC) based on the homomorphic properties of cryptosystem. Among the cryptosytem with homomorphic properties BGN is preferable, because it is partially doubly homomorphic, which can perform arbitrary number of addition and only one multiplication. But the main disadvantage of using BGN cryptosystem is its execution time. In proposed system, the execution time is reduced by a factor of 12 by replacing conventional composite order group by prime order group. This leads to an efficient secure speaker recognition.

scholarly journals Secure Speaker Recognition using BGN Cryptosystem with Prime Order Bilinear Group

Cryptography ◽  
2020 ◽  
pp. 277-294
Author(s):  
S. Selva Nidhyananthan ◽  
M. Prasad ◽  
R. Shantha Selva Kumari
Keyword(s):  
Speaker Recognition ◽  
Execution Time ◽  
Prime Order ◽  
Speaker Identification ◽  
Speaker Verification ◽  
Recognition System ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Speech Input ◽  
Order Group

Speech being a unique characteristic of an individual is widely used in speaker verification and speaker identification tasks in applications such as authentication and surveillance respectively. In this paper, framework for secure speaker recognition system using BGN Cryptosystem, where the system is able to perform the necessary operations without being able to observe the speech input provided by the user during speaker recognition process. Secure speaker recognition makes use of Secure Multiparty Computation (SMC) based on the homomorphic properties of cryptosystem. Among the cryptosytem with homomorphic properties BGN is preferable, because it is partially doubly homomorphic, which can perform arbitrary number of addition and only one multiplication. But the main disadvantage of using BGN cryptosystem is its execution time. In proposed system, the execution time is reduced by a factor of 12 by replacing conventional composite order group by prime order group. This leads to an efficient secure speaker recognition.

Speaker Verification and Identification

2010 ◽  
pp. 264-289
Author(s):  
Minho Jin ◽  
Chang D. Yoo
Keyword(s):  
Feature Extraction ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Speaker Verification ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Essential Elements ◽  
Verification System ◽  
Biometric Characteristic ◽  
Speaker Modeling

A speaker recognition system verifies or identifies a speaker’s identity based on his/her voice. It is considered as one of the most convenient biometric characteristic for human machine communication. This chapter introduces several speaker recognition systems and examines their performances under various conditions. Speaker recognition can be classified into either speaker verification or speaker identification. Speaker verification aims to verify whether an input speech corresponds to a claimed identity, and speaker identification aims to identify an input speech by selecting one model from a set of enrolled speaker models. Both the speaker verification and identification system consist of three essential elements: feature extraction, speaker modeling, and matching. The feature extraction pertains to extracting essential features from an input speech for speaker recognition. The speaker modeling pertains to probabilistically modeling the feature of the enrolled speakers. The matching pertains to matching the input feature to various speaker models. Speaker modeling techniques including Gaussian mixture model (GMM), hidden Markov model (HMM), and phone n-grams are presented, and in this chapter, their performances are compared under various tasks. Several verification and identification experimental results presented in this chapter indicate that speaker recognition performances are highly dependent on the acoustical environment. A comparative study between human listeners and an automatic speaker verification system is presented, and it indicates that an automatic speaker verification system can outperform human listeners. The applications of speaker recognition are summarized, and finally various obstacles that must be overcome are discussed.

scholarly journals A simulation study on optimal scores for speaker recognition

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Dong Wang
Keyword(s):  
Simulation Study ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Speaker Verification ◽  
Real Life ◽  
Recognition System ◽  
Single Form ◽  
Recognition Systems ◽  
Minimum Bayes Risk ◽  
Cosine Distance

AbstractIn this article, we conduct a comprehensive simulation study for the optimal scores of speaker recognition systems that are based on speaker embedding. For that purpose, we first revisit the optimal scores for the speaker identification (SI) task and the speaker verification (SV) task in the sense of minimum Bayes risk (MBR) and show that the optimal scores for the two tasks can be formulated as a single form of normalized likelihood (NL). We show that when the underlying model is linear Gaussian, the NL score is mathematically equivalent to the PLDA likelihood ratio (LR), and the empirical scores based on cosine distance and Euclidean distance can be seen as approximations of this linear Gaussian NL score under some conditions.Based on the unified NL score, we conducted a comprehensive simulation study to investigate the behavior of the scoring component on both the SI task and SV task, in the case where the distribution of the speaker vectors perfectly matches the assumption of the NL model, as well as the case where some mismatch is involved. Importantly, our simulation is based on the statistics of speaker vectors derived from a practical speaker recognition system, hence reflecting the behavior of the NL scoring in real-life scenarios that are full of imperfection, including non-Gaussianality, non-homogeneity, and domain/condition mismatch.

scholarly journals A CNN based Speaker Recognition System using an Alternate Bone Microphone

2019 ◽  
Vol 9 (2) ◽  
pp. 4224-4227
Keyword(s):  
Speaker Recognition ◽  
Input Data ◽  
Speaker Identification ◽  
Recognition System ◽  
The Other ◽  
Identification System ◽  
Video Recorder ◽  
State Of Art

State-of-art speaker recognition system uses acoustic microphone speech to identify/verify a speaker. The multimodal speaker recognition system includes modality of input data recorded using sources like acoustics mic,array mic ,throat mic, bone mic and video recorder. In this paper we implemented a multi-modal speaker identification system with three modality of speech as input, recorded from different microphones like air mic, throat mic and bone mic . we propose and claim an alternate way of recording the bone speech using a throat microphone and the results of a implemented speaker recognition using CNN and spectrogram is presented. The obtained results supports our claim to use the throat microphone as suitable mic to record the bone conducted speech and the accuracy of the speaker recognition system with signal speech recorded from air microphone get improved about 10% after including the other modality of speech like throat and bone speech along with the air conducted speech.

Pertinent Prosodic Features for Speaker Identification by Voice

2012 ◽  
pp. 227-241
Author(s):  
Halim Sayoud ◽  
Siham Ouamour
Keyword(s):  
Vector Quantization ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Speaker Verification ◽  
Low Frequency ◽  
Arabic Language ◽  
Prosodic Features ◽  
Acoustic Features ◽  
Heterogeneous Features ◽  
The Mean

Most existing systems of speaker recognition use “state of the art” acoustic features. However, many times one can only recognize a speaker by his or her prosodic features, especially by the accent. For this reason, the authors investigate some pertinent prosodic features that can be associated with other classic acoustic features, in order to improve the recognition accuracy. The authors have developed a new prosodic model using a modified LVQ (Learning Vector Quantization) algorithm, which is called MLVQ (Modified LVQ). This model is composed of three reduced prosodic features: the mean of the pitch, original duration, and low-frequency energy. Since these features are heterogeneous, a new optimized metric has been proposed that is called Optimized Distance for Heterogeneous Features (ODHEF). Tests of speaker identification are done on Arabic corpus because the NIST evaluations showed that speaker verification scores depend on the spoken language and that some of the worst scores were got for the Arabic language. Experimental results show good performances of the new prosodic approach.

SPEAKER IDENTIFICATION BY AGGREGATING GAUSSIAN MIXTURE MODELS (GMMs) BASED ON UNCORRELATED MFCC-DERIVED FEATURES

2014 ◽  
Vol 28 (04) ◽  
pp. 1456006 ◽  
Author(s):  
AMITA PAL ◽  
SMARAJIT BOSE ◽  
GOPAL K. BASAK ◽  
AMITAVA MUKHOPADHYAY
Keyword(s):  
Mixture Models ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Gaussian Mixture Models ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Recognition System ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Corpus ◽  
Signal Process

For solving speaker identification problems, the approach proposed by Reynolds [IEEE Signal Process. Lett.2 (1995) 46–48], using Gaussian Mixture Models (GMMs) based on Mel Frequency Cepstral Coefficients (MFCCs) as features, is one of the most effective available in the literature. The use of GMMs for modeling speaker identity is motivated by the interpretation that the Gaussian components represent some general speaker-dependent spectral shapes, and also by the capability of Gaussian mixtures to model arbitrary densities. In this work, we have initially illustrated, with the help of a new bilingual speech corpus, how the well-known principal component transformation, in conjunction with the principle of classifier combination can be used to enhance the performance of the MFCC-GMM speaker recognition systems significantly. Subsequently, we have emphatically and rigorously established the same using the benchmark speech corpus NTIMIT. A significant outcome of this work is that the proposed approach has the potential to enhance the performance of any speaker recognition system based on correlated features.

scholarly journals IDENTIFIKASI SUARA MENGGUNAKAN METODE MEL FREQUENCY CEPSTRUM COEFFICIENTS (MFCC) DAN JARINGAN SYARAF TIRUAN BACKPROPAGATION

DIELEKTRIKA ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 48
Author(s):  
Erina Nursholihatun ◽  
Sudi Mariyanto Sasongko ◽  
Abdullah Zainuddin
Keyword(s):  
Pattern Recognition ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Rejection Rate ◽  
Recognition System ◽  
Signal To Noise ◽  
Characteristic Extraction ◽  
Average Accuracy ◽  
Noise Ratio

The voice is basic humans tool of communications. Speakers identifications is the process of recoqnizing the identity of a speaker by comparing the inputed voice features with all the features of each speaker in the database.There are two step of speaker identification process: feature extraction and pattern recognition. For the characteristic extraction phase using Mel Frequency Cepstrum Coefficient (MFCC) method. The method of pattern recognition using backpropagation artificial neural networks that compares the test data with the reference data in the database based on the variable result in the learning process. The result from the research show that increasing SNR (Signal to Noise Ratio) value will determine the success of the speaker recognition system. The higher SNR (Signal to Noise Ratio), will increase percentage level of recognition. Average accuracy speakers recoqnition of the speakers data without noise generating is 86%, the biggest average accuracy speakers recoqnition is  92 % in the data with 80 dB SNR level, and the lowest average accuracy is  45 % in the data with 80 dB SNR level. Rejection rate testing result of speakers outside the database is 100 %.

scholarly journals A Novel Approach to Increase the Efficiency of a Multi-lingual Real-time Speaker Identification System

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Real Time ◽  
Speech Enhancement ◽  
Speaker Recognition ◽  
Speaker Identification ◽  
Cost Effective ◽  
Recognition System ◽  
Identification System ◽  
Novel Approach ◽  
Activity Method ◽  
Voice Activity

Nowadays, the real-time speaker recognition system is very popular due to its cost-effective nature. However, it is a very challenging one to produce a more efficient speaker identification system. In our work, we work on a multi-lingual real-time speaker identification system. We work in a novel way to enhance the efficiency of the said system. We take some real speech signals and use different speech enhancement methods and our proposed voice activity method (VAD) to enhance the efficiency of said system. By doing so, we increase the accuracy of the said system relatively by 2% as compared to existing methods.

scholarly journals Development of High Accuracy Classifier for the Speaker Recognition System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Raghad Tariq Al-Hassani ◽  
Dogu Cagdas Atilla ◽  
Çağatay Aydin
Keyword(s):  
Speaker Recognition ◽  
Recognition Accuracy ◽  
Speaker Identification ◽  
Time Domain Analysis ◽  
Recognition System ◽  
Training Data ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Proposed Model ◽  
Pitch Frequency

Speech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), K -nearest neighbour (KNN), and support vector machine (SVM).

Pertinent Prosodic Features for Speaker Identification by Voice

2010 ◽  
Vol 2 (2) ◽  
pp. 18-33
Author(s):  
Halim Sayoud ◽  
Siham Ouamour
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
State Of The Art ◽  
Speaker Verification ◽  
Low Frequency ◽  
Arabic Language ◽  
Prosodic Features ◽  
Acoustic Features ◽  
Heterogeneous Features ◽  
The Mean

Most existing systems of speaker recognition use “state of the art” acoustic features. However, many times one can only recognize a speaker by his or her prosodic features, especially by the accent. For this reason, the authors investigate some pertinent prosodic features that can be associated with other classic acoustic features, in order to improve the recognition accuracy. The authors have developed a new prosodic model using a modified LVQ (Learning Vector Quantization) algorithm, which is called MLVQ (Modified LVQ). This model is composed of three reduced prosodic features: the mean of the pitch, original duration, and low-frequency energy. Since these features are heterogeneous, a new optimized metric has been proposed that is called Optimized Distance for Heterogeneous Features (ODHEF). Tests of speaker identification are done on Arabic corpus because the NIST evaluations showed that speaker verification scores depend on the spoken language and that some of the worst scores were got for the Arabic language. Experimental results show good performances of the new prosodic approach.

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